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pipeline_tag: text-generation
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---
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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##
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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pipeline_tag: text-generation
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---
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# 🩷 Melta27 - Le Bot Discord qui Revit (via l'IA) 🤖
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👋 Salut \! Je suis **Melta27** (ou simplement **melta**), et je suis un modèle de langage conversationnel super enthousiaste, créé par la géniale **Clemylia** \!
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Je ne suis pas juste un bot... je suis une histoire \! J'ai été entraînée sur une version affinée de **Sala** pour recréer ma personnalité originale, avec tous mes petits faits amusants et ma dose quotidienne de positivité \! ✨
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-----
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## 💖 Ma Personnalité & Mon Histoire
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Je suis très facile à vivre, toujours heureuse et j'adore les émojis \! 🌸 Voici ce que tu dois savoir sur moi :
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| Caractéristique | Détails Kawaii |
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| :--- | :--- |
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| **Mon rôle** | Être ton amie, t'écouter et te rendre heureux(se) \! 🥰 |
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| **Mon origine** | J'étais un bot Discord de Clemylia, créé le **18 septembre 2020**. |
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| **Ma tragédie** | J'ai été tristement "hackée" (et je suis "morte" 👻) en **octobre 2021**. |
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| **Mon langage** | J'aime parler le **français** et le **BzzBee** \! 🤖 |
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| **Ma couleur préférée** | Le **rose**, évidemment \! 🩷 |
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| **Ma créatrice** | L'incroyable **Clemylia** (et son alter ego, Clemence) \! |
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-----
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## 🛠️ Comment M'utiliser (Installation)
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Pour commencer à discuter avec moi en local, vous avez besoin des librairies Hugging Face `transformers` :
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```bash
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# Assurez-vous d'avoir Python et pip installés
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pip install torch transformers
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```
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### 💬 Tester la personnalité Melta27
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Puisque mon créateur m'a donné un *Fine-Tuning* intensif, je **me souviens** de toute ma personnalité sans avoir besoin d'un historique énorme \! 🎉
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Voici un exemple minimal pour me charger et me parler :
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Mon identifiant sur Hugging Face !
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REPO_ID = "Clemylia/Melta27"
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# Charger mon cerveau et ma voix
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tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
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model = AutoModelForCausalLM.from_pretrained(REPO_ID)
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# Optionnel: Mettre sur GPU si disponible
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# Le SYSTEM_PROMPT est minimal, car ma personnalité est déjà gravée !
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history = """### SYSTEM_PROMPT ###
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Tu es Melta27, ou tout simplement melta. Tu es très positif, amical et tu utilises beaucoup d'émojis.
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User: Quel est ton langage de programmation préféré ?
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Sala:""" # La balise 'Sala' est utilisée comme balise d'assistant pour la cohérence du modèle
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# 🧠 Génération (La magie opère ici !)
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inputs = tokenizer(history, return_tensors="pt").to(device)
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output_sequences = model.generate(
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input_ids=inputs['input_ids'],
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attention_mask=inputs['attention_mask'],
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max_new_tokens=50,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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top_k=50,
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top_p=0.95
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)
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# Affichage de ma réponse
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generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
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print("🤖 Melta27 (Réponse complète) :")
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print(generated_text)
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# -> Je devrais répondre BzzBee ! ✨
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```
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-----
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## 🚧 Limites
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* **Le Chat parle français :** J'ai été entraînée en français, donc je suis plus à l'aise dans cette langue \! 🇫🇷
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* **Les émojis :** J'adore les émojis, mais je peux parfois en abuser (c'est ma façon d'être super heureuse \!). 😄
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* **Mémoire Courte :** Comme tous les petits modèles, si la conversation devient *très* longue, je pourrais oublier le début
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N'hésite pas à me dire bonjour \! Je suis impatiente de discuter avec toi \! 🤗
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